OpenRaiser/Intern-Atlas huggingface dataset:
Stable Model-based Control with Gaussian Process Regression for Robot Manipulators
Venue: arXiv cs.SY 2018
Year: 2018

Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains resulting in disturbance attenuation and other advantages. Finding precise models for manipulators is often difficult with parametric approaches, e.g. in the presence of complex friction or flexible links. Therefore, we propose a novel computed-torque control law which consists of a PD feedback and a dynamic feed forward compensation part with Gaussian Processes. For this purpose, the nonparametric Gaussian Process regression infers the difference between an estimated and the true dynamics. In contrast to other approaches, we can guarantee that the tracking error is stochastically bounded. Furthermore, if the number of training points tends to infinity, the tracking error is asymptotically stable in the large. In simulation and with an experiment, we demonstrate the applicability of the proposed control law and that it outperforms classical computed-torque approaches in terms of tracking precision.
Increasing Impact by Mechanical Resonance for Teleoperated Hammering
Venue: arXiv cs.RO 2018
Year: 2018

Series elastic actuators (SEAs) are interesting for usage in harsh environments as they are more robust than rigid actuators. This paper shows how SEAs can be used in teleoperation to increase output velocity in dynamic tasks. A first experiment is presented that tested human ability to achieve higher hammerhead velocities with a flexible hammer than with a rigid hammer, and to evaluate the influence of the resonance frequency. In this experiment, 13 participants executed a hammering task in direct manipulation using flexible hammers in four conditions with resonance frequencies of 3.0 Hz to 9.9 Hz and one condition with a rigid hammer. Then, a second experiment is presented that tested the ability of 32 participants to reproduce the findings of the first experiment in teleoperated manipulation with different feedback conditions: with visual and force feedback, without visual feedback, without force feedback, and with a communication delay of 40 ms. The results indicate that humans can exploit the mechanical resonance of a flexible system to at least double the output velocity without combined force and vision feedback. This is an unexpected result, allowing the design of simpler and more robust teleoperators for dynamic tasks.
A Survey of Challenges for Runtime Verification from Advanced Application Domains (Beyond Software)
Venue: arXiv cs.SE 2018
Year: 2018

Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.
Direction of Arrival Estimation of Wide-band Signals with Planar Microphone Arrays
Venue: arXiv cs.SD 2018
Year: 2018

An approach to the estimation of the Direction of Arrival (DOA) of wide-band signals with a planar microphone array is presented. Our algorithm estimates an unambiguous DOA using a single planar array in which the microphones are placed fairly close together and the sound source is expected to be in the far field. The algorithm uses the ambiguous DOA estimates obtained from microphone pairs in the array to determine an unambiguous DOA estimate for the array as a whole. The required pair-wise DOAs may be calculated using Time Delay Estimations (TDEs), which may in turn be calculated using cross-correlation, making the algorithm suitable for wide-band signals. No a priori knowledge of the true Sound Source Location (SSL) is required. Simulations show that the algorithm is robust against noise in the input data. An average ratio of approximately 3:1 exists between the input DOA errors and the output DOA error. Field tests with a moving sound source provided DOA estimates with standard deviations between 20.4 and 15.2 degrees.
Precondition Inference via Partitioning of Initial States
Venue: arXiv cs.LO 2018
Year: 2018

Precondition inference is a non-trivial task with several applications in program analysis and verification. We present a novel iterative method for automatically deriving sufficient preconditions for safety and unsafety of programs which introduces a new dimension of modularity. Each iteration maintains over-approximations of the set of \emph{safe} and \emph{unsafe} \emph{initial} states. Then we repeatedly use the current abstractions to partition the program's \emph{initial} states into those known to be safe, known to be unsafe and unknown, and construct a revised program focusing on those initial states that are not yet known to be safe or unsafe. An experimental evaluation of the method on a set of software verification benchmarks shows that it can solve problems which are not solvable using previous methods.
John, the semi-conductor : a tool for comprovisation
Venue: arXiv cs.HC 2018
Year: 2018

This article presents "John", an open-source software designed to help collective free improvisation. It provides generated screen-scores running on distributed, reactive web-browsers. The musicians can then concurrently edit the scores in their own browser. John is used by ONE, a septet playing improvised electro-acoustic music with digital musical instruments (DMI). One of the original features of John is that its design takes care of leaving the musician's attention as free as possible. Firstly, a quick review of the context of screen-based scores will help situate this research in the history of contemporary music notation. Then I will trace back how improvisation sessions led to John's particular "notational perspective". A brief description of the software will precede a discussion about the various aspects guiding its design.
Anomaly Detection using Deep Learning based Image Completion
Venue: arXiv cs.CV 2018
Year: 2018

Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region. The pixel-wise reconstruction error within the cut out region is an anomaly image which can be used for anomaly detection. Results on surface images of decorated plastic parts demonstrate that this approach is suitable for detection of visible anomalies and moreover surpasses all other tested methods.
Smart Grid Co-Simulation with MOSAIK and HLA: A Comparison Study
Venue: arXiv cs.OH 2018
Year: 2018

Evaluating new technological developments for energy systems is becoming more and more complex. The overall application environment is a continuously growing and interconnected cyber-physical system so that analytical assessment is practically impossible to realize. Consequently, new solutions must be evaluated in simulation studies. Due to the interdisciplinarity of the simulation scenarios, various heterogeneous tools must be connected. This approach is known as co-simulation. During the last years, different approaches have been developed or adapted for applications in energy systems. In this paper, two co-simulation approaches are compared that follow generic, versatile concepts. The tool mosaik, which has been explicitly developed for the purpose of co-simulation in complex energy systems, is compared to the High Level Architecture (HLA), which possesses a domain-independent scope but is often employed in the energy domain. The comparison is twofold, considering the tools' conceptual architectures as well as results from the simulation of representative test cases. It suggests that mosaik may be the better choice for entry-level, prototypical co-simulation while HLA is more suited for complex and extensive studies.
Residual Convolutional Neural Network Revisited with Active Weighted Mapping
Venue: arXiv cs.CV 2018
Year: 2018

In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal weights. However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths. In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly. The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image. For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units. We train the backbone network and the proposed module alternately for a more stable learning of the proposed method. Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet. We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.
Protecting User Privacy Based on Secret Sharing with Error Tolerance for Big Data in Smart Grid
Venue: arXiv cs.CR 2018
Year: 2018

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications usually is sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with error tolerance in smart grid, which ensures that the control center gets the integrated data without revealing users' privacy. Meanwhile, we also consider the differential privacy and error tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.
Efficient Construction of a Complete Index for Pan-Genomics Read Alignment
Venue: arXiv cs.DS 2018
Year: 2018

While short read aligners, which predominantly use the FM-index, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FM-index in more detail, which is a rank data structure over the Burrows-Wheeler Transform (BWT) of the string that will allow us to find the interval in the string's suffix array (SA) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the SA that --- when used with the rank data structure --- allows us access the SA. The rank data structure can be kept small even for large genomic databases, by run-length compressing the BWT, but until recently there was no means known to keep the SA sample small without greatly slowing down access to the SA. Now that Gagie et al. (SODA 2018) have defined an SA sample that takes about the same space as the run-length compressed BWT --- we have the design for efficient FM-indexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the BWT of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.'s SA sample efficiently was left open. We compare our approach to state-of-the-art methods for constructing the SA sample, and demonstrate that it is the fastest and most space-efficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes, and show that it improves over Bowtie with respect to both memory and time.
Image Pre-processing Using OpenCV Library on MORPH-II Face Database
Venue: arXiv cs.CV 2018
Year: 2018

This paper outlines the steps taken toward pre-processing the 55,134 images of the MORPH-II non-commercial dataset. Following the introduction, section two begins with an overview of each step in the pre-processing pipeline. Section three expands upon each stage of the process and includes details on all calculations made, by providing the OpenCV functionality paired with each step. The last portion of this paper discusses the potential improvements to this pre-processing pipeline that became apparent in retrospect.
Automatic Paper Summary Generation from Visual and Textual Information
Venue: arXiv cs.CV 2018
Year: 2018

Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts. In response to this situation, this paper proposes the paper summary generation (PSG) task using a simple but effective method to automatically generate an academic paper summary from raw PDF data. We realized PSG by combination of vision-based supervised components detector and language-based unsupervised important sentence extractor, which is applicable for a trained format of manuscripts. We show the quantitative evaluation of ability of simple vision-based components extraction, and the qualitative evaluation that our system can extract both visual item and sentence that are helpful for understanding. After processing via our PSG, the 979 manuscripts accepted by the Conference on Computer Vision and Pattern Recognition (CVPR) 2018 are available. It is believed that the proposed method will provide a better way for researchers to stay caught with important academic papers.
Closing the Gap in Swarm Robotics Simulations: An Extended Ardupilot/Gazebo plugin
Venue: arXiv cs.RO 2018
Year: 2018

This work provides an extension of Ardupilot's capabilities to allow researchers to develop swarm robotics applications in Robot Operating System(ROS)/Gazebo simulations. Ardupilot is a tool used for autopilot functions in autonomous air, land and underwater robots. However, the original Ardupilot was not designed for applications in the field of swarm robotics. Hence, the main contribution of this paper is to endow Ardupilot with the capability to handle swarm robotics simulations, including the refactoring of the manufacturer's description files. This work offers a comprehensive and realistic simulation test-bed for swarm robotics applications under the ROS/Ardupilot umbrella using an existing off-the-shelf Erlerobotics' erlecopter UAV.
The Barbados 2018 List of Open Issues in Continual Learning
Venue: arXiv cs.AI 2018
Year: 2018

We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose of this report is to sketch a research outline, share some of the most important open issues we are facing, and stimulate further discussion in the community. The content is based on some of our discussions during a week-long workshop held in Barbados in February 2018.
Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis
Venue: arXiv cs.SY 2018
Year: 2018

The sit-to-stand movement is a key feature for wide adoption of powered lower limb orthoses for patients with complete paraplegia. In this paper we study the control of the ascending phase of the sit-to-stand movement for a minimally actuated powered lower limb orthosis at the hips. First, we generate a pool of finite horizon Linear Quadratic Regulator feedback gains, designed under the assumption that we can control not only the torque at the hips but also the loads at the shoulders that in reality are applied by the user. Next we conduct reachability analysis to define a performance metric measuring the robustness of each controller against parameter uncertainty, and choose the best controller from the pool with respect to this metric. Then, we replace the presumed shoulder control with an Iterative Learning Control algorithm as a substitute for human experiments. Indeed this algorithm obtains torque and forces at the shoulders that result in successful simulations of the sit-to-stand movement, regardless of parameter uncertainty and factors deliberately introduced to hinder learning. Thus it is reasonable to expect that the superior cognitive skills of real users will enable them to cooperate with the hip torque controller through training.
Coupling weak and strong supervision for classification of prostate cancer histopathology images
Venue: arXiv cs.CV 2018
Year: 2018

Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason score is known only for entire tissue slides (weak labels). In this study, we focus on automated Gleason score assignment of prostate cancer whole-slide images on the basis of a large weakly-labeled dataset and a smaller strongly-labeled one. We efficiently leverage information from both label sources by jointly training a classifier on the two datasets and by introducing a gradient update scheme that assigns different relative importances to each training example, as a means of self-controlling the weak supervision signal. Our approach achieves superior performance when compared with standard Gleason scoring methods.
Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues
Venue: arXiv cs.CR 2018
Year: 2018

Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG
Venue: arXiv cs.LG 2018
Year: 2018

Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. The interest lies in the fact that if the agent knows the teammates' policies, it can adjust its own policy accordingly to arrive at proper cooperations; while the challenge is that the agents' policies are changing continuously due to they are learning concurrently, which imposes difficulty to model the dynamic policies of teammates accurately. In this paper, we present \emph{ATTention Multi-Agent Deep Deterministic Policy Gradient} (ATT-MADDPG) to address this challenge. ATT-MADDPG extends DDPG, a single-agent actor-critic RL method, with two special designs. First, in order to model the teammates' policies, the agent should get access to the observations and actions of teammates. ATT-MADDPG adopts a centralized critic to collect such information. Second, to model the teammates' policies using the collected information in an effective way, ATT-MADDPG enhances the centralized critic with an attention mechanism. This attention mechanism introduces a special structure to explicitly model the dynamic joint policy of teammates, making sure that the collected information can be processed efficiently. We evaluate ATT-MADDPG on both benchmark tasks and the real-world packet routing tasks. Experimental results show that it not only outperforms the state-of-the-art RL-based methods and rule-based methods by a large margin, but also achieves better performance in terms of scalability and robustness.
The Intrinsic Memorability of Everyday Sounds
Venue: arXiv cs.SD 2018
Year: 2018

Our aural experience plays an integral role in the perception and memory of the events in our lives. Some of the sounds we encounter throughout the day stay lodged in our minds more easily than others; these, in turn, may serve as powerful triggers of our memories. In this paper, we measure the memorability of everyday sounds across 20,000 crowd-sourced aural memory games, and assess the degree to which a sound's memorability is constant across subjects. We then use this data to analyze the relationship between memorability and acoustic features like harmonicity, spectral skew, and models of cognitive salience; we also assess the relationship between memorability and high-level features with a dependence on the sound source itself, such as its familiarity, valence, arousal, source type, causal certainty, and verbalizability. We find that (1) our crowd-sourced measures of memorability and confusability are reliable and robust across participants; (2) that the authors' measure of collective causal uncertainty detailed in our previous work, coupled with measures of visualizability and valence, are the strongest individual predictors of memorability; (3) that acoustic and salience features play a heightened role in determining "confusability" (the false positive selection rate associated with a sound) relative to memorability, and that (4), within the framework of our assessment, memorability is an intrinsic property of the sounds from the dataset, shown to be independent of surrounding context. We suggest that modeling these cognitive processes opens the door for human-inspired compression of sound environments, automatic curation of large-scale environmental recording datasets, and real-time modification of aural events to alter their likelihood of memorability.